Ovarian CystOCIDNCT05342298
Assessment of Ovarian Cysts Using Machine Learning
An observational study of Ovarian Cyst, run by Assiut University at 2 sites in Egypt.
Trial summary
The study aims at creating a prediction model using machine learning algorithms that is capable of predicting malignant potential of ovarian cysts/masses based on patient characteristics, sonographic findings, and biochemical markers Read more about this study
- Who is it for?
- Ages 15 to 80 years, women onlyHealthy volunteers can take part
- Conditions the study is for
- Ovarian Cyst
- What is being tested?
- prediction model
- How many people
- 1,000 participants
- Where
- 2 sitesEgypt
- Study dates
- 1 Oct 2022to 1 Sep 2023
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 15 to 80 years
- Women only
- Healthy volunteers accepted
- Child, Adult, Older adult
Study population: Any female who are postmenarchal, have documented follow-up for at least 1 year following initial presentation unless surgically managed, and provide authorization to use their medical records for research purposes. They should have received their care in the receptive centers.
Who may be able to join
- Females who are postmenarchal, have documented follow-up for at least 1 year following initial presentation unless surgically managed, and provide authorization to use their medical records for research purposes. They should have received their care in the receptive centers
Who cannot join
- Women will be excluded from the study if they were admitted for an acute event including cyst torsion, rupture or hemorrhage with no prior documentation of ovarian cysts. Women with cysts smaller than 3 cm will not be eligible.
Trial locations and contacts
2 sites in 1 country.
Egypt2 sites
Alexandria University Main Hospital
Alexandria, 21516
Ahmed H. Ismail, 01144557597, ahmed.ismail.mogge@gmail.com
Assiut University
Asyut, 71511
Manar M. Ahmed, 01128793950, manar.mahran.mogge@gmail.com
About this study
Official title: Prediction of Malignant Potential of Ovarian Cysts Using Machine Learning Models
The study aims at creating a prediction model using machine learning algorithms that is capable of predicting malignant potential of ovarian cysts/masses based on patient characteristics, sonographic findings, and biochemical markers
Read the detailed description
Ovarian cysts are one of the most common gynecologic disorders encountered in clinical practice. Approximately 20% of women may experience ovarian cysts at least once in their lifetime. However, incidence of significant ovarian cysts is 8% in premenopausal. In fact, many ovarian cysts are discovered incidentally while pelvic imaging is done for other indications. Interestingly, prevalence of ovarian cysts may reach up to 14-18% in menopausal women, many of which are likely persistent (2). Although most ovarian cysts are benign, definitive diagnosis cannot be made based on one time sonographic findings. Simple cysts are typically benign. Complex and solid cysts are still likely benign. However, malignancy is more common in this group of cysts. Definitive diagnosis by histopathology warrants surgical removal of the cyst/ovary. Because the condition is common and is mostly benign, surgery is not considered unless malignancy is reasonably a concern or the cyst is symptomatic.
Therefore, most ovarian cysts are expectantly managed. Aim of expectant management is to determine cyst changes. Follow-up may extend beyond a year. However, recommendations have not been consistent among internationally recognized guidelines, and different cut-offs of cyst size and different frequencies and durations of follow-up were considered (5, 6). Similarly, there are different systems that are adopted by these guidelines to triage women with ovarian cysts based on sonographic and biochemical indicators.
This project aims at creating a prediction model using machine learning algorithms that can be applied to women with ovarian cysts. The aim of this mode is to determine probability of cancer and management plan including surgery, long-term or short-term follow-up.
Retrieved records will be reviewed for eligibility. Patients will be considered for inclusion if they are postmenarchal, have documented follow-up for at least 1 year following initial presentation unless surgically managed, and provide authorization to use their medical records for research purposes. They should have received their care in the receptive centers. Women will be excluded from the study if they were admitted for an acute event including cyst torsion, rupture or hemorrhage with no prior documentation of ovarian cysts. Women with cysts smaller than 3 cm will not be eligible.
A standardized data collection spreadsheet is designed for the purpose of the study and will be shared with all contributing centers. Data collection will include patient demographics (e.g., age, parity, body mass index, ethnicity, smoking status), gynecologic history (e.g., menstrual abnormalities, contraceptive status), medical history (e.g., including chronic health issues and personal history of cancers), surgical history, family history of cancers including any diagnosed familial cancer syndromes. Specific information on current presentation will comprise presenting symptoms, if any, relevant physical signs, sonographic features (e.g., cyst size, side, consistency, locularity, presence of septa, solid areas, papillae, intracystic fluid texture, associated pelvic fluid or ascites), features noted in other imaging modalities if any, tumor markers (CA125, HCG, ALP, LDH,HE-4), management plan including surgical findings and histopathological diagnosis, follow-up including follow-up findings and cyst/mass complications during follow-up.
- Conditions
- Ovarian Cyst
- Being tested
- prediction model
- Keywords
- machine learning, ovarian masses, ovarian cystectomy
- Medical subject headings
- Ovarian Cysts
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
prediction modelOther
Data will be pre-processed prior to final analysis, including data cleaning, imputation of missing values, dimensionality reduction, and removal of outliers. Data will be utilized as Xi and Yi where Xi presents input (features) and Yi presents dependent variables (outcomes). Different classification algorithms will be tested for accuracy to build the final model including logistic regression, SVM, XGboost and random forest algorithms. Data will be split at 0.8:0.2 for model training and testing, respectively.
What is being measured
The primary outcome is the main question the study is designed to answer.
Final diagnosis of ovarian cyst type
Diagnosis of whether the cyst is benign or malignant based on histopathology, or cyst resolution or shrinkage on follow-up
Measured: Within 3 years of diagnosis of ovarian cyst
Secondary outcomes (1)
Incidence of acute events during follow-up and prior to final diagnosis.
Incidence of ovarian torsion, cyst rupture and intra-abdominal hemorrhage requiring surgical intervention
Measured: Within 3 years of diagnosis of ovarian cyst
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted22 Apr 2022
- Study start1 Oct 2022 (estimated)
- Primary completion1 Jun 2023 (estimated)
- Study completion1 Sep 2023 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (6)Papers the sponsor has linked to this study.
- Other trials for Ovarian CystStudies of the same condition, open and under way.
- More trials run by Assiut UniversityThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Retrospective
- Enrolment
- 1,000 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Assiut University
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Sherif Abdelkarim Mohammed Shazly, Assiut University
- Investigators
- Sherif Shazly, MSc, Study director, Assiut University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05342298
- Sponsor's protocol ID
- MCOG-AI02
- Acronym
- OCID
- Status last verified
- Aug 2022
- First submitted
- 16 Apr 2022
- Last updated
- 23 Aug 2022
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Undecided
Publications
Ross EK, Kebria M. Incidental ovarian cysts: When to reassure, when to reassess, when to refer. Cleve Clin J Med. 2013 Aug;80(8):503-14. doi: 10.3949/ccjm.80a.12155.
PubMed 23908107Mobeen S, Apostol R. Ovarian Cyst. 2023 Jun 5. In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2026 Jan-. Available from http://www.ncbi.nlm.nih.gov/books/NBK560541/
PubMed 32809376Boos J, Brook OR, Fang J, Brook A, Levine D. Ovarian Cancer: Prevalence in Incidental Simple Adnexal Cysts Initially Identified in CT Examinations of the Abdomen and Pelvis. Radiology. 2018 Jan;286(1):196-204. doi: 10.1148/radiol.2017162139. Epub 2017 Sep 14.
PubMed 28914598Farghaly SA. Current diagnosis and management of ovarian cysts. Clin Exp Obstet Gynecol. 2014;41(6):609-12.
PubMed 25551948Shazly, S.; Laughlin-Tommaso, S.K. Ovarian Tumors. In Gynecology: A CREOG and Board Exam Review; Springer International Publishing: Cham, Switzerland, 2020; pp. 489-519.
Mehasseb MK, Siddiqui NA, Bryden F. The Management of Ovarian Cysts in Postmenopausal Women. Royal College of Obstetricians and Gynaecologist. RCOG Green-top Guideline. 2016;34:1-31.
Study at a glance
From the registry record, last updated by the sponsor on 23 Aug 2022. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Prediction of Malignant Potential of Ovarian Cysts Using Machine Learning Models
- ClinicalTrials.gov ID
- NCT05342298
- Acronym / sponsor ID
- OCID / MCOG-AI02
- Conditions
- Ovarian Cyst
- Keywords
- machine learning, ovarian masses, ovarian cystectomy
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 1,000 participants (estimated)
- Lead sponsor
- Assiut UniversityUniversity, hospital or other
- Study start
- 1 Oct 2022 (estimated)
- Primary completion
- 1 Jun 2023 (estimated)
- Study completion
- 1 Sep 2023 (estimated)
- First posted
- 22 Apr 2022 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
The study aims at creating a prediction model using machine learning algorithms that is capable of predicting malignant potential of ovarian cysts/masses based on patient characteristics, sonographic findings, and biochemical markers
Detailed description
Ovarian cysts are one of the most common gynecologic disorders encountered in clinical practice. Approximately 20% of women may experience ovarian cysts at least once in their lifetime. However, incidence of significant ovarian cysts is 8% in premenopausal. In fact, many ovarian cysts are discovered incidentally while pelvic imaging is done for other indications. Interestingly, prevalence of ovarian cysts may reach up to 14-18% in menopausal women, many of which are likely persistent (2). Although most ovarian cysts are benign, definitive diagnosis cannot be made based on one time sonographic findings. Simple cysts are typically benign. Complex and solid cysts are still likely benign. However, malignancy is more common in this group of cysts. Definitive diagnosis by histopathology warrants surgical removal of the cyst/ovary. Because the condition is common and is mostly benign, surgery is not considered unless malignancy is reasonably a concern or the cyst is symptomatic.
Therefore, most ovarian cysts are expectantly managed. Aim of expectant management is to determine cyst changes. Follow-up may extend beyond a year. However, recommendations have not been consistent among internationally recognized guidelines, and different cut-offs of cyst size and different frequencies and durations of follow-up were considered (5, 6). Similarly, there are different systems that are adopted by these guidelines to triage women with ovarian cysts based on sonographic and biochemical indicators.
This project aims at creating a prediction model using machine learning algorithms that can be applied to women with ovarian cysts. The aim of this mode is to determine probability of cancer and management plan including surgery, long-term or short-term follow-up.
Retrieved records will be reviewed for eligibility. Patients will be considered for inclusion if they are postmenarchal, have documented follow-up for at least 1 year following initial presentation unless surgically managed, and provide authorization to use their medical records for research purposes. They should have received their care in the receptive centers. Women will be excluded from the study if they were admitted for an acute event including cyst torsion, rupture or hemorrhage with no prior documentation of ovarian cysts. Women with cysts smaller than 3 cm will not be eligible.
A standardized data collection spreadsheet is designed for the purpose of the study and will be shared with all contributing centers. Data collection will include patient demographics (e.g., age, parity, body mass index, ethnicity, smoking status), gynecologic history (e.g., menstrual abnormalities, contraceptive status), medical history (e.g., including chronic health issues and personal history of cancers), surgical history, family history of cancers including any diagnosed familial cancer syndromes. Specific information on current presentation will comprise presenting symptoms, if any, relevant physical signs, sonographic features (e.g., cyst size, side, consistency, locularity, presence of septa, solid areas, papillae, intracystic fluid texture, associated pelvic fluid or ascites), features noted in other imaging modalities if any, tumor markers (CA125, HCG, ALP, LDH,HE-4), management plan including surgical findings and histopathological diagnosis, follow-up including follow-up findings and cyst/mass complications during follow-up.
Study design
- Observational model
- Cohort
- Time perspective
- Retrospective
Who may qualify
- Ages
- 15 to 80 years
- Sex
- Female only
- Healthy volunteers
- Accepted
- Age groups
- Child, Adult, Older adult
- Study population
- Any female who are postmenarchal, have documented follow-up for at least 1 year following initial presentation unless surgically managed, and provide authorization to use their medical records for research purposes. They should have received their care in the receptive centers.
Key inclusion criteria (1 listed)
- Females who are postmenarchal, have documented follow-up for at least 1 year following initial presentation unless surgically managed, and provide authorization to use their medical records for research purposes. They should have received their care in the receptive centers
Key exclusion criteria (1 listed)
- Women will be excluded from the study if they were admitted for an acute event including cyst torsion, rupture or hemorrhage with no prior documentation of ovarian cysts. Women with cysts smaller than 3 cm will not be eligible.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| prediction modelData will be pre-processed prior to final analysis, including data cleaning, imputation of missing values, dimensionality reduction, and removal of outliers. Data will be utilized as Xi and Yi where Xi presents input (features) and Yi presents dependent variables (outcomes). Different classification algorithms will be tested for accuracy to build the final model including logistic regression, SVM, XGboost and random forest algorithms. Data will be split at 0.8:0.2 for model training and testing, respectively. | Other | – |
Primary outcome
Final diagnosis of ovarian cyst type
Time frame: Within 3 years of diagnosis of ovarian cyst
Diagnosis of whether the cyst is benign or malignant based on histopathology, or cyst resolution or shrinkage on follow-up
1 secondary outcome
Incidence of acute events during follow-up and prior to final diagnosis.
Time frame: Within 3 years of diagnosis of ovarian cyst
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 23 Aug 2022.
- Background: PubMed 23908107Ross EK, Kebria M. Incidental ovarian cysts: When to reassure, when to reassess, when to refer. Cleve Clin J Med. 2013 Aug;80(8):503-14. doi: 10.3949/ccjm.80a.12155.
- Background: PubMed 32809376Mobeen S, Apostol R. Ovarian Cyst. 2023 Jun 5. In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2026 Jan-. Available from http://www.ncbi.nlm.nih.gov/books/NBK560541/
- Background: PubMed 28914598Boos J, Brook OR, Fang J, Brook A, Levine D. Ovarian Cancer: Prevalence in Incidental Simple Adnexal Cysts Initially Identified in CT Examinations of the Abdomen and Pelvis. Radiology. 2018 Jan;286(1):196-204. doi: 10.1148/radiol.2017162139. Epub 2017 Sep 14.
- Background: PubMed 25551948Farghaly SA. Current diagnosis and management of ovarian cysts. Clin Exp Obstet Gynecol. 2014;41(6):609-12.
- BackgroundShazly, S.; Laughlin-Tommaso, S.K. Ovarian Tumors. In Gynecology: A CREOG and Board Exam Review; Springer International Publishing: Cham, Switzerland, 2020; pp. 489-519.
- BackgroundMehasseb MK, Siddiqui NA, Bryden F. The Management of Ovarian Cysts in Postmenopausal Women. Royal College of Obstetricians and Gynaecologist. RCOG Green-top Guideline. 2016;34:1-31.
- Individual participant dataSharing planned: Undecided
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Sherif Shazly, MSc study director
Assiut University
2 sites in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Ovarian Cyst, run by Assiut University at 2 sites in Egypt.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 1 inclusion and 1 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: status not confirmed, as of the sponsor's last update on 23 Aug 2022. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 2 sites in Egypt. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
Contact Sherif Shazly, MSc (+4407554480388, sherif.shazly.mogge@gmail.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Final diagnosis of ovarian cyst type (time frame: Within 3 years of diagnosis of ovarian cyst). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT05342298, which also lists 6 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 23 Aug 2022.